Sonar has long been used for detecting objects on the ocean bottom. A sonar image typically is comprised of a matrix of points or picture elements (pixels) displayed on either a cathode ray tube or paper. The points or pixels have a greyness level ranging from 0 to 255 on a greyness scale. Objects normally appear on sonar images as a highlight shadow combination, particularly when the sonar images are generated by a moving vehicle. A sonar scan for short distances typically comprises a massive amount of data which must be reviewed in short time periods. Prior to the present invention this review was done by human analysts looking at the sonar scans. As the analyst reviews the scans he marks areas where he has inferred specific objects or features may be present on the ocean bottom. Because of the massive amounts of data that must be reviewed in short time periods, human analysts are subject to frequent errors or omissions in both detection and classification.
Reliable automatic, machine discrimination between man-made and natural objects in sonar images has not been achieved in the past. Machine processing methods developed for photograph and video images do not work well for noisy sonar images.
Sonar images differ in two major ways from video images. Sonar images contain much more noise than video images. The noise is multiplicative and the signal to noise ratio is much lower. Secondly, sonar images in general contain much less information about an object than do video images. Thus, image segmentation techniques based on edge detection, gradient thinning and texture recognition do not work reliably. Without specific shapes to find, template matching is useless. Moreover, sonar images are highly aspect angle and grazing angle dependent so that the same object will appear differently when viewed from different angles and ranges.
The relatively low information content of these images is another reason that previous attempts at machine classification of objects in sonar images as man-made or natural have failed. Even human analysts have difficulty doing this and often make mistakes.
Accordingly, there is a need for an automatic target cuing system for sonar images. The system must have extremely high throughput rates of many hundreds of thousands of sonar picture elements (pixels) per second. Such high rates preclude the use of complicated algorithms on single CPU architectures. Finally, the system should have applicability beyond sonar images to video pictures and other images comprised of a matrix of pixels having known greyness levels.